BackgroundSH2B1β is a signaling adaptor protein that has been shown to promote neuronal differentiation in PC12 cells and is necessary for the survival of sympathetic neurons. However, the mechanism by which SH2B1β may influence cell survival is not known.ResultsIn this study, we investigated the role of SH2B1β in oxidative stress-induced cell death. Our results suggest that overexpressing SH2B1β reduced H2O2-induced, caspase 3-dependent apoptosis in PC12 cells and hippocampal neurons. In response to H2O2, overexpressing SH2B1β enhanced PI3K (phosphatidylinositol 3-kinas)-AKT (protein kinase B) and MEK (MAPK/ERK kinase)-extracellular-signal regulated kinases 1 and 2 (ERK1/2) signaling pathways. We further demonstrated that SH2B1β was able to reduce H2O2-induced nuclear localization of FoxO1 and 3a transcription factors, which lie downstream of PI3K-AKT and MEK-ERK1/2 pathways. Moreover, overexpressing SH2B1β reduced the expression of Fas ligand (FasL), one of the target genes of FoxOs.ConclusionsOverexpressing the adaptor protein SH2B1β enhanced H2O2-induced PI3K-AKT and MEK-ERK1/2 signaling, reduced nucleus-localized FoxOs and the expression of a pro-apoptotic gene, FasL.

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This paper addresses two augmentation problems related to bipartite graphs. The first, a fundamental graph-theoretical problem, is how to add a set of edges with the smallest possible cardinality so that the resulting graph is 2-edgeconnected, i.e., bridge-connected, and still bipartite. The second problem, which arises naturally from research on the security of statistical data, is how to add edges so that the resulting graph is simple and does not contain any bridges. In both cases, after adding edges, the graph can be either a simple graph or, if necessary, a multi-graph. Our approach then determines whether or not such an augmentation is possible.We propose a number of simple linear-time algorithms to solve both problems. Given the well-known bridge-block data structure for an input graph, the algorithms run in O(log n) parallel time on an EREW PRAM using a linear number of processors, where n is the number of vertices in the input graph. We note that there is already a polynomial time algorithm that solves the first augmentation problem related to graphs with a given general partition constraint in O(n(m + n log n) log n) 354 Algorithmica (2009) 54: 353-378 time, where m is the number of distinct edges in the input graph. We are unaware of any results for the second problem.

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